Deep Teleportation: Quantum Simulation of Conscious Report in Attentional Blink

TL;DR

This paper presents a quantum model using a three-qubit entanglement circuit to simulate conscious reporting in the attentional blink paradigm, successfully reproducing key experimental phenomena like lag 1 sparing and masking effects.

Contribution

It introduces a novel deep teleportation quantum circuit model to simulate consciousness-related phenomena in attention tasks, improving alignment with human data.

Findings

Successfully simulated lag 1 sparing effect

Reproduced lag 7 divergence in responses

Captured masking effects in quantum model

Abstract

Recent quantum models of cognition have successfully simulated several interesting effects in human experimental data, from vision to reasoning and recently even consciousness. The latter case, consciousness has been a quite challenging phenomenon to model, and most efforts have been through abstract mathematical quantum methods, mainly focused on conceptual issues. Classical (non-quantum) models of consciousness-related experiments exist, but they generally fail to align well with human data. We developed a straightforward quantum model to simulate conscious reporting of seeing or missing competing stimuli within the famous attentional blink paradigm. In an attentional blink task, a target stimulus (T2) that appears after a previous one (T1) can be consciously reported if the delay between presenting them is short enough (called lag 1), otherwise it can be rendered invisible during the so-called refractory period of attention (lags 2 to 6 and even longer). For modeling this phenomenon, we employed a three-qubit entanglement ansatz circuit in the form of a deep teleportation channel instead of the well-known EPR channel. While reporting the competing stimuli was supposed to be the classical measurement outcomes, the effect of distractor stimuli (i.e., masks, if any) was encoded simply as random angle rotations. The simulation outcome for different states was measured, and the classical outcome probabilities were further used as inputs to a simple linear neural network. The result revealed a non-linear, alternating state pattern that closely mirrors human responses in conscious stimuli reporting. The main result was a successful simulation of Lag 1 sparing, lag 7 divergence, and masking effect through probabilistic outcome of measurement in different conditions.